Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

Abstract

Abstract-Poor illumination, less background contrast and blurring effects makes the medical, satellite and camera images difficult to visualize. Image fusion plays the vital role to enhance image quality by resolving the above issues and reducing the image quantity. The combination of spatial and spectral technique Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) decrease processing time and reduce number of dimensions but down sampling causes lack of shift invariance that results in poor quality final fused image. At first this work uses combined median and average filter that eliminates noise in the image which is caused by illumination, camera circuitry and sensor at preprocessing stage. Then, hybrid Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) technique is implemented to increase output image accuracy by eliminating down sampling and is not influenced by artifacts and blurring effects. Further, it can overcome the trade-off of Heisenberg's uncertainty principle by improving accuracy in both domains, time (spatial) as well as frequency (spectral). The proposed combined median and average filter with hybrid SWT-PCA algorithm measures quality parameters, such as peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC) and improved results depict the superiority of the algorithm than existing techniques.

A Review of Digital Image Fusion and its Application

“…FUSION 1) It is utilized in satellite or remote space due to suitable view of satellite vision [66][67][68].…”

An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

“…However, the poor illumination and blurring effect degrades the fused image so it cannot preserve the detailed information from input images. The edge-preserving filter based image fusion is proposed by Tian et al [4] using combined median-average based discrete stationary wavelet transform (DSWT) with PCA. At first, the median-average filter eliminates the noise This work is licensed under a Creative Commons Attribution 4.0 License.…”

“…The performance evaluation is categorized in subjective and objective evaluation. We have compared the experimental results with PCA [22], DWT [23], median-average based DSWT-PCA [4], non-subsampled shearlet transform based spatial frequency and pulse code neural network (NSST-SF-PCNN) [26] and morphology-hat transform based contourlet transform with principal component analysis (MT-CT-PCA) [28] image fusion techniques. The median-average based DSWT-PCA, NSST-SF-PCNN and MT-CT-PCA are new hybrid image fusion algorithms, which perform better than other techniques but the proposed method achieves the best performance among the aforementioned schemes.…”

“…The image fusion is the way of merging multiple input images to produce one output image which extracts high quality and more informative for the perception of human vision, robot and other processing tasks as compared to any of the input images [1][2][3]. In recent years, image fusion techniques have become a promising research area and have received a lot of interest in many applications such as computer vision, face detection and recognition, medical diagnose, surveillance, and so on [1,[4][5][6].…”

A Deep Hybrid Few Shot Divide and Glow Method for Ill-Light Image Enhancement

“…Authors in [22] presented a joint enhancement and denoising method (JED) following sequential decomposition, but the denoising operation often produces indiscriminate smoothness. Low light color image contrast enhancement methods [23] [24] [25] follow the image fusion strategy. Another illumination-based feature fusion strategy for weaklit images (FEW) is proposed in [26]; however, it sacrifices the details in the extremely short exposure and rich texture regions.…”